A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage
Divorce will have destructive spiritual and material effects, and unfortunately, in this regard recent statistics have shown that solutions provided for its prevention and reduction have not been effective. One of the effective solutions to reduce divorce in society is to review the background of th...
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| Format: | Article |
| Language: | English |
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University of science and culture
2022-01-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_165859_20df4bbeda23e6e2ee59837103faad7b.pdf |
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| author | Touba Torabipour Safieh Siadat Hosein Taghavi |
| author_facet | Touba Torabipour Safieh Siadat Hosein Taghavi |
| author_sort | Touba Torabipour |
| collection | DOAJ |
| description | Divorce will have destructive spiritual and material effects, and unfortunately, in this regard recent statistics have shown that solutions provided for its prevention and reduction have not been effective. One of the effective solutions to reduce divorce in society is to review the background of the couple, which can provide valuable experiences to experts, and used by experts and family counselors. In this article, a method has been proposed that uses data mining and deep learning to help family counselors to predict the outcome of marriage as a practical tool. Reviewing the background of thousands of couples will provide a model for the coupe behavior analysis. The primary data of this study was collected from the information of 35,000 couples registered in the National Organization for Civil Registration of Iran during 2018-2019. In the current work, we proposed a method to predict divorce by combining a convolutional neural network (CNN) and long short-term memory (LSTM). In this hybrid method, key features in a dataset are selected using CNN layers, and then predicted using LSTM layers with an accuracy of 99.67 percent. A comparison of the method used in this article and Multilayer Perceptron (MLP) and CNN suggests that it has a higher degree of accuracy. |
| format | Article |
| id | doaj-art-f6cfa139c5bd4bc08ecb6d8d55834028 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-f6cfa139c5bd4bc08ecb6d8d558340282025-01-08T07:33:20ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-01-0151889510.22133/ijwr.2023.375686.1147A data Mining Approach using CNN and LSTM to Predict Divorce before MarriageTouba Torabipour0Safieh Siadat 1Hosein Taghavi2Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran,IranDepartment of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran,IranDepartment of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran,IranDivorce will have destructive spiritual and material effects, and unfortunately, in this regard recent statistics have shown that solutions provided for its prevention and reduction have not been effective. One of the effective solutions to reduce divorce in society is to review the background of the couple, which can provide valuable experiences to experts, and used by experts and family counselors. In this article, a method has been proposed that uses data mining and deep learning to help family counselors to predict the outcome of marriage as a practical tool. Reviewing the background of thousands of couples will provide a model for the coupe behavior analysis. The primary data of this study was collected from the information of 35,000 couples registered in the National Organization for Civil Registration of Iran during 2018-2019. In the current work, we proposed a method to predict divorce by combining a convolutional neural network (CNN) and long short-term memory (LSTM). In this hybrid method, key features in a dataset are selected using CNN layers, and then predicted using LSTM layers with an accuracy of 99.67 percent. A comparison of the method used in this article and Multilayer Perceptron (MLP) and CNN suggests that it has a higher degree of accuracy.https://ijwr.usc.ac.ir/article_165859_20df4bbeda23e6e2ee59837103faad7b.pdfpredictiondivorcedata mininglstm |
| spellingShingle | Touba Torabipour Safieh Siadat Hosein Taghavi A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage International Journal of Web Research prediction divorce data mining lstm |
| title | A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage |
| title_full | A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage |
| title_fullStr | A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage |
| title_full_unstemmed | A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage |
| title_short | A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage |
| title_sort | data mining approach using cnn and lstm to predict divorce before marriage |
| topic | prediction divorce data mining lstm |
| url | https://ijwr.usc.ac.ir/article_165859_20df4bbeda23e6e2ee59837103faad7b.pdf |
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